Mauro, Gerardo Maria (2015) Multi-objectvice optimization for cost-optimal energy retrofitting: from the single building to a stock. [Tesi di dottorato]

[img]
Preview
Text
mauro_gerardomaria_27.pdf

Download (7MB) | Preview
[error in script] [error in script]
Item Type: Tesi di dottorato
Lingua: English
Title: Multi-objectvice optimization for cost-optimal energy retrofitting: from the single building to a stock
Creators:
CreatorsEmail
Mauro, Gerardo Mariagerar.mauro@gmail.com
Date: 30 March 2015
Number of Pages: 194
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Industriale
Scuola di dottorato: Ingegneria industriale
Dottorato: Ingegneria dei sistemi meccanici
Ciclo di dottorato: 27
Coordinatore del Corso di dottorato:
nomeemail
Bozza, Fabiofabio.bozza@unina.it
Tutor:
nomeemail
Bianco, NicolaUNSPECIFIED
Vanoli, Giuseppe PeterUNSPECIFIED
Date: 30 March 2015
Number of Pages: 194
Uncontrolled Keywords: building energy retrofit, cost-optimal analysis, building performance simulation, building category, reference buildings, multi-objecive optimization, genetic algorithms, uncertainty analysis, sensitivity analysis, artificial neural networks, EnergyPlus, Matlab
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-IND/10 - Fisica tecnica industriale
Area 09 - Ingegneria industriale e dell'informazione > ING-IND/11 - Fisica tecnica ambientale
Aree tematiche (7° programma Quadro): ENERGIA > Efficienza e risparmi energetico
Date Deposited: 13 Apr 2015 10:50
Last Modified: 25 Sep 2015 08:21
URI: http://www.fedoa.unina.it/id/eprint/10292
DOI: 10.6092/UNINA/FEDOA/10292

Abstract

The energy retrofitting of the existing building stock is a key strategy to achieve tangible results in the reduction of worldwide energy consumption and, thus, polluting emissions. However, the path is very challenging. Indeed, the design of the building energy retrofit is a complex and arduous task, which involves two distinct perspectives: the collective (state) one, interested in energy savings, and the private (single building) one, interested in economic benefits. The Energy Performance of Buildings Directive Recast (2010/31/EU) aims to harmonize such perspectives by prescribing the cost-optimal analysis in order to detect the best packages of energy retrofit measures. This procedure yields a series of critical, still-open questions that have aroused a heated discussion in the scientific community. Among them, the main questions, identified in this study, can be outlined as follows: How to perform a reliable cost-optimal analysis of the retrofit measures for a single building? How to achieve global indications about the cost-optimality of energy retrofitting the existing building stock? How to evaluate the global cost of a building with a minimum computational time and a good reliability? A definitive and robust answer to these questions is fundamental to overcome the main obstacle to the large diffusion of the cost-optimal retrofitting practice. Such obstacle can be summarized in a last crucial question that includes the previous ones: How to perform a reliable, fast, 'ad hoc' cost-optimal analysis of the energy retrofit measures for each building of the stock? So far, the scientific literature did not propose a full and complete response to such critical questions. This thesis aims to provide a thorough answer to the aforementioned questions, by means of an original approach that handles all the issues involved in a robust and reliable cost-optimal analysis, achievable for every single building with an acceptable computational burden and complexity. Three novel methodologies (CAMO, SLABE, building energy simulation by ANNs) have been developed for proposing a complete response to the first three questions and, then, they are combined in a macro multi-stage methodology (CASA) that solves the final fundamental question, which represents the last step towards a wide-spread cost-optimal building retrofitting. The methodologies are tested by means of the application to different case studies. CASA is a step towards sustainability.

Actions (login required)

View Item View Item